Python à Grande-Bretagne

Senior Data Engineer (AI & MLOps) - Software - Newcastle/Hybrid or Remote

Day rate: £300 - £500 (Inside IR35)

Duration: 6 months

Start: ASAP

My new client is looking for a Senior Data Engineer with expertise in AI, MLOps, and AWS architecture to design and deliver production-grade machine learning pipelines. The ideal candidate will be passionate about bridging the gap between data science experimentation and scalable production systems, driving automation, and enabling faster innovation cycles.

• Architect, build, and maintain production-grade ML Ops pipelines to automate deployment, monitoring, and scaling of machine learning models.
• Collaborate with data scientists and ML engineers to reduce time-to-production for experiments and prototypes.
• Design and optimize data wrangling and transformation workflows using Python.
• Leverage AWS cloud services (EC2, S3, Lambda, SageMaker, RDS, DynamoDB, Redshift, etc.) to build robust, scalable, and cost-effective solutions.
• Apply AIOps practices to enhance monitoring, automation, and resilience of ML systems.
• Implement best practices in data engineering, version control, CI/CD, and infrastructure as code.
• Ensure the security, reliability, and compliance of data pipelines and deployed ML solutions.
• Mentor junior engineers and contribute to setting technical standards for the team.

• Proven experience as a Senior Data Engineer, MLOps Engineer, or similar role.
• Strong background in data structures, algorithms, and software engineering principles.
• Advanced proficiency in Python for data wrangling, pipeline automation, and ML workflows.
• Expertise in AWS services, including databases (RDS, DynamoDB, Redshift) and machine learning/AI (SageMaker, AI/ML frameworks).
• Hands-on experience with ML pipeline orchestration, CI/CD, and deployment automation.
• Deep understanding of ML Ops practices, including monitoring, scaling, and retraining strategies.
• Familiarity with AIOps concepts and tools for operational automation.

• Experience with data science and machine learning model development.
• Knowledge of containerization (Docker, Kubernetes, EKS).
• Exposure to infrastructure-as-code (Terraform, CloudFormation).
• Strong problem-solving, communication, and collaboration skills.

Rates depend on experience and client requirements